ADL-YOLOv8: A Field Crop Weed Detection Model Based on Improved YOLOv8

文献类型： 外文期刊

第一作者： Jia, Zhiyu

作者： Jia, Zhiyu;Yuan, Chang;Liu, Hongrui;Shi, Jinlong;Zhang, Ming;Liu, Qinghua;Qiu, Xiulin;Zhao, Weiguo;Zhao, Weiguo

作者机构：

关键词： weed detection; ADL-YOLOv8; lightweight model; precision agriculture; attention mechanism

期刊名称：AGRONOMY-BASEL （ 影响因子：3.4； 五年影响因子：3.8 ）

ISSN：

年卷期： 2024 年 14 卷 10 期

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收录情况： SCI

摘要： This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, the research not only reduces the hardware costs of automated impurity removal equipment but also enhances software recognition accuracy, contributing to reduced pesticide use and the promotion of sustainable agriculture. The ADL-YOLOv8 model incorporates a lighter AKConv network for better processing of specific features, an ultra-lightweight DySample upsampling module to improve accuracy and efficiency, and the LSKA-Attention mechanism for enhanced detection, particularly of small targets. On the same dataset, ADL-YOLOv8 demonstrated a 2.2% increase in precision, a 2.45% rise in recall, a 3.07% boost in mAP@0.5, and a 1.9% enhancement in mAP@0.95. The model's size was cut by 15.77%, and its computational complexity was reduced by 10.98%. These findings indicate that ADL-YOLOv8 not only exceeds the original YOLOv8n model but also surpasses the newer YOLOv9t and YOLOv10n in overall performance. The improved algorithm model makes the hardware cost required for embedded terminals lower.

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